{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as  plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[11, 12, 13, 14, 15],\n",
       "       [16, 17, 18, 19, 20],\n",
       "       [21, 22, 23, 24, 25],\n",
       "       [26, 27, 28, 29, 30],\n",
       "       [31, 32, 33, 34, 35]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[11,12,13,14,15],[16,17,18,19,20],[21,22,23,24,25],[26,27,28,29,30],[31,32,33,34,35]])\n",
    "a.shape\n",
    "print(a[2,4])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>电影名称</th>\n",
       "      <th>打斗镜头</th>\n",
       "      <th>接吻镜头</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California Man</td>\n",
       "      <td>3</td>\n",
       "      <td>101</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>He's Not Really into Dudes</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beautiful Woman</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin Longblade</td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Robo Slayer 3000</td>\n",
       "      <td>99</td>\n",
       "      <td>5</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amped ll</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         电影名称  打斗镜头  接吻镜头 label\n",
       "0              California Man     3   101   爱情片\n",
       "1  He's Not Really into Dudes     2   100   爱情片\n",
       "2             Beautiful Woman     1    81   爱情片\n",
       "3             Kevin Longblade   101    10   动作片\n",
       "4            Robo Slayer 3000    99     5   动作片\n",
       "5                    Amped ll    98     2   动作片"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = {\n",
    "    '电影名称': ['California Man','He\\'s Not Really into Dudes','Beautiful Woman',' Kevin Longblade','Robo Slayer 3000','Amped ll'],\n",
    "    '打斗镜头': [3,2,1,101,99,98],\n",
    "    '接吻镜头': [101,100,81,10,5,2],\n",
    "    'label': ['爱情片','爱情片','爱情片','动作片','动作片','动作片'],\n",
    "}\n",
    "df = pd.DataFrame(date)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'''\n",
    "#k 近邻算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'kiss')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df.iloc[:,1],y=df.iloc[:,2],c=['green','green','green','red','red','red'])\n",
    "plt.xlabel('fight')\n",
    "plt.ylabel('kiss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[len(df)] ={\n",
    "    '电影名称': '?',\n",
    "    '打斗镜头': 18,\n",
    "    '接吻镜头': 90,\n",
    "    'label': '?',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'kiss')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FhSTJ4/GEfJ/H4wnuO5qCggK53e7gIyMjo+1+CQAAEFUxHTuBQECDBg3Svffeq4EDB2rq1KmaMmWKFi5c2KrnnT17tnw+X/BRXl4epokBAECsienY6d69u84444yQbaeffrr27NkjSfJ6vZKkysrKkGMqKyuD+44mMTFRLpcr5AEAAOwU07EzfPhwlZWVhWzbsWOHevXqJUnKzMyU1+tVYWFhcL/f71dxcbFycnIiOisAAIhN8dEe4FhuvvlmDRs2TPfee6+uuuoqlZSUaNGiRVq0aJEkyeFwKD8/X/fcc4/69u2rzMxMzZkzR+np6Ro7dmx0hwcAADEhpmPnnHPO0apVqzR79mzdfffdyszM1COPPKK8vLzgMbfffrsOHjyoqVOnqrq6Wuedd55ef/11dezYMYqTAwCAWBHT99mJFO6zAwBA+2PFfXYAAABai9gBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYrUWxU15ers8++yz4dUlJifLz87Vo0aKwDQYAABAOLYqdq6++Whs2bJAkVVRU6Ic//KFKSkp0xx136O677w7rgAAAAK3RotjZvn27hg4dKkl6/vnnddZZZ+ntt9/W0qVLtWTJknDOBwAA0Cotip26ujolJiZKkt544w2NGTNGkpSVlaV9+/aFbzoAAIBWalHsnHnmmVq4cKHeeustrVu3Tpdeeqkkae/everWrVtYBwQAAGiNFsXO/fffr6eeekoXXHCBJk6cqAEDBkiSXn755eDLWwAAALHAYYwxLfnGhoYG+f1+de3aNbjtH//4hzp16qS0tLSwDRgJfr9fbrdbPp9PLpcr2uMAAIBmaO7f7xad2fnmm29UW1sbDJ3du3frkUceUVlZWbsLHQAAYLcWxc7ll1+uZ599VpJUXV2t7OxsPfTQQxo7dqyefPLJsA4IAADQGi2Knffee08/+MEPJEkvvPCCPB6Pdu/erWeffVbz588P64AAAACt0aLY+frrr5WcnCxJ+vOf/6xx48bJ6XTq3HPP1e7du8M6IAAAQGu0KHb69Omj1atXq7y8XGvXrtUll1wiSaqqquICXwAAEFNaFDtz587Vrbfeqt69eys7O1s5OTmSGs/yDBw4MKwDAgAAtEaL33peUVGhffv2acCAAXI6G5uppKRELpdLWVlZYR2yrfHWcwAA2p/m/v2Ob+kP8Hq98nq9Idu4oSAAAIg1zY6dcePGacmSJXK5XLriiivkcDiaPHblypVhGQ4AAKC1mh07brc7GDgpKSlNHnesCAIAAIi0ZsfO4sWLg/++5JJLNHHixKMed9ttt7V+KgAAgDBp0buxrr/+er322mtHbJ85c6aee+65Vg8FAAAQLi2KnaVLl2rixInavHlzcNuMGTO0fPlybdiwIWzDAQAAtFaLYmfUqFFasGCBxowZo9LSUt1www1auXKlNm7c2O7edg4AAOzW4reeX3311aqurtbw4cN18skna9OmTerTp084ZwMAAGi1ZsfOzJkzj7r95JNP1qBBg7RgwYLgtocffrj1kwEAAIRBs2Nn69atR93ep08f+f3+4H7eeg4AAGJJs2OHC48BAEB71KILlAEAANoLYgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAVmtXsXPffffJ4XAoPz8/uO3QoUOaNm2aunXrpi5dumj8+PGqrKyM3pAAACCmtJvY2bJli5566in1798/ZPvNN9+sV155RStWrNCmTZu0d+9ejRs3LkpTAgCAWNMuYqempkZ5eXl6+umn1bVr1+B2n8+nZ555Rg8//LAuuugiDR48WIsXL9bbb7+td955J4oTAwCAWNEuYmfatGkaNWqUcnNzQ7aXlpaqrq4uZHtWVpZ69uypoqKiJp+vtrZWfr8/5AEAAOwUH+0Bvsvy5cv13nvvacuWLUfsq6ioUEJCglJSUkK2ezweVVRUNPmcBQUFuuuuu8I9KgAAiEExfWanvLxcN910k5YuXaqOHTuG7Xlnz54tn88XfJSXl4ftuQEAQGyJ6dgpLS1VVVWVBg0apPj4eMXHx2vTpk2aP3++4uPj5fF4dPjwYVVXV4d8X2Vlpbxeb5PPm5iYKJfLFfIAAAB2iumXsS6++GJ98MEHIduuueYaZWVl6Ze//KUyMjLUoUMHFRYWavz48ZKksrIy7dmzRzk5OdEYGQAAxJiYjp3k5GSdddZZIds6d+6sbt26Bbdfd911mjlzplJTU+VyuTRjxgzl5OTo3HPPjcbIAAAgxsR07DTHb37zGzmdTo0fP161tbUaMWKEFixYEO2xAABAjHAYY0y0h4g2v98vt9stn8/H9TsAALQTzf37HdMXKAMAALQWsQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwWkzHTkFBgc455xwlJycrLS1NY8eOVVlZWcgxhw4d0rRp09StWzd16dJF48ePV2VlZZQmBgAAsSamY2fTpk2aNm2a3nnnHa1bt051dXW65JJLdPDgweAxN998s1555RWtWLFCmzZt0t69ezVu3LgoTg0AAGKJwxhjoj1Ec33xxRdKS0vTpk2bdP7558vn8+nkk0/WsmXL9OMf/1iS9PHHH+v0009XUVGRzj333GY9r9/vl9vtls/nk8vlastfAQAAhElz/37H9Jmd/+Tz+SRJqampkqTS0lLV1dUpNzc3eExWVpZ69uypoqKiJp+ntrZWfr8/5AEAAOzUbmInEAgoPz9fw4cP11lnnSVJqqioUEJCglJSUkKO9Xg8qqioaPK5CgoK5Ha7g4+MjIy2HB0AAERRu4mdadOmafv27Vq+fHmrn2v27Nny+XzBR3l5eRgmBAAAsSg+2gM0x/Tp07VmzRq9+eab6tGjR3C71+vV4cOHVV1dHXJ2p7KyUl6vt8nnS0xMVGJiYluODAAAYkRMn9kxxmj69OlatWqV1q9fr8zMzJD9gwcPVocOHVRYWBjcVlZWpj179ignJyfS4wIAgBgU02d2pk2bpmXLlumll15ScnJy8Doct9utpKQkud1uXXfddZo5c6ZSU1Plcrk0Y8YM5eTkNPudWAAAwG4x/dZzh8Nx1O2LFy/W5MmTJTXeVPCWW27RH/7wB9XW1mrEiBFasGDBMV/G+k+89RwAgPanuX+/Yzp2IoXYAQCg/bHyPjsAAADHi9gBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAbefAAemrryRjojYCsQMAAMJv1Srp3HMll0tKTZVOPVV69FGpvj7ioxA7AAAgvO67Txo3Ttqy5V/bdu+Wbr5ZuuoqqaEhouMQOwAAIHw++kiaPbvx34HAv7Yb0/hYtUpaujSiIxE7AAAgfJ56SoqPb3q/0yk9/njk5hGxAwAAwun99499XU4gIG3fHrl5ROwAAIBw6ty58ezNsSQlRWaW/4/YAQAA4TNuXOi1Ov8pPl668srIzSNiBwAAhNOECVJGhhQXd+Q+p7Nxe35+REcidgAAQPh06iRt2CD17t34dXx848PhkLp0kdaskbKyIjrSMS6XBgAAaIHTTpM+/lh69VVp7Vqprk4aOlSaOLExeCKM2AEAAOEXHy9dfnnjI8p4GQsAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAx88Y6YsvpH37jn3H5BhA7AAAgOYzRnruOal/fyktTUpPl3r1kv73f4/9AaBRROwAAIDmmztX+ulPpQ8//Ne2zz6Tbr9d+vGPpYaG6M3WBGIHAAA0z9at0j33NP77P1+6MkZ66SVp2bLIz/UdiB0AANA8Tz3VeGfkpjid0uOPR26eZiJ2AABA83zwwbGvywkEQl/eihHEDgAAaJ7k5MZPLz+Wzp0jM8txIHYAAEDz/PjHjdfmNCUuTpowIXLzNBOxAwAAmufqqxvfZn6063acTikxUbrxxsjP9R2IHQAA0DydOknr10unntr4dYcOjQ9J6tpV+vOf/7UvhhzjkmoAAID/cOqp0kcfSWvXNj7q66WcnMaXuBIToz3dURE7AACgaV9/Lf3ud41vO9+zR+rWTbrmGun666XLLov2dM3iMOZYVxqdGPx+v9xut3w+n1wuV7THAQAgNvh80kUXNd5MUPrXxclOp+T1Sps3S5mZURuvuX+/uWYHAAAc3cyZ0vvvN0bOv58bCQSkqqqYfOfV0RA7AADgSPv3N37gZ1OfdVVfL5WU/OusTwwjdgAAwJG2bZMOHz72MQ6HVFQUkXFag9gBAABHiov77mOMad5xUUbsAACAI51zTuPHQ3yX3Ny2n6WViB0AAHCkTp2k6dOb/iysuDhp9GjptNMiO1cLWBM7TzzxhHr37q2OHTsqOztbJSUl0R4JAID27a67pHHjGv/97UdEfPuy1aBB0u9/H525jpMVsfPHP/5RM2fO1J133qn33ntPAwYM0IgRI1RVVRXt0QAAaL86dJBWrJDWrWu8Q/LQodLIkdLzz0t/+UvjR0S0A1bcVDA7O1vnnHOOHn/8cUlSIBBQRkaGZsyYoVmzZn3n93NTQQAA2p8T5qaChw8fVmlpqXL/7QIpp9Op3NxcFTXxdrja2lr5/f6QBwAAsFO7j50vv/xSDQ0N8ng8Ids9Ho8qKiqO+j0FBQVyu93BR0ZGRiRGBQAAUdDuY6clZs+eLZ/PF3yUl5dHeyQAANBG2v2nnp900kmKi4tTZWVlyPbKykp5vd6jfk9iYqISY/Rj6AEAQHi1+zM7CQkJGjx4sAoLC4PbAoGACgsLlZOTE8XJAABALGj3Z3YkaebMmZo0aZKGDBmioUOH6pFHHtHBgwd1zTXXRHs0AAAQZVbEzn//93/riy++0Ny5c1VRUaGzzz5br7/++hEXLQMAgBOPFffZaS3uswMAQPtzwtxnBwAA4FiIHQAAYDUrrtlprW9fyeNOygAAtB/f/t3+rityiB1JBw4ckCTupAwAQDt04MABud3uJvdzgbIa78uzd+9eJScny+FwtPh5/H6/MjIyVF5ezoXObYh1jgzWOTJY58hgnSMnkmttjNGBAweUnp4up7PpK3M4s6PGDw7t0aNH2J7P5XLxP6YIYJ0jg3WODNY5MljnyInUWh/rjM63uEAZAABYjdgBAABWI3bCKDExUXfeeScfMtrGWOfIYJ0jg3WODNY5cmJxrblAGQAAWI0zOwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7YfLEE0+od+/e6tixo7Kzs1VSUhLtkdq1goICnXPOOUpOTlZaWprGjh2rsrKykGMOHTqkadOmqVu3burSpYvGjx+vysrKKE1sh/vuu08Oh0P5+fnBbaxzeHz++ef6yU9+om7duikpKUn9+vXTu+++G9xvjNHcuXPVvXt3JSUlKTc3Vzt37ozixO1TQ0OD5syZo8zMTCUlJem0007Tr3/965DPTmKtj9+bb76p0aNHKz09XQ6HQ6tXrw7Z35w13b9/v/Ly8uRyuZSSkqLrrrtONTU1kfkFDFpt+fLlJiEhwfzud78zf/vb38yUKVNMSkqKqaysjPZo7daIESPM4sWLzfbt2822bdvMyJEjTc+ePU1NTU3wmF/84hcmIyPDFBYWmnfffdece+65ZtiwYVGcun0rKSkxvXv3Nv379zc33XRTcDvr3Hr79+83vXr1MpMnTzbFxcXm008/NWvXrjWffPJJ8Jj77rvPuN1us3r1avP++++bMWPGmMzMTPPNN99EcfL2Z968eaZbt25mzZo1ZteuXWbFihWmS5cu5tFHHw0ew1ofvz/96U/mjjvuMCtXrjSSzKpVq0L2N2dNL730UjNgwADzzjvvmLfeesv06dPHTJw4MSLzEzthMHToUDNt2rTg1w0NDSY9Pd0UFBREcSq7VFVVGUlm06ZNxhhjqqurTYcOHcyKFSuCx3z00UdGkikqKorWmO3WgQMHTN++fc26devMf/3XfwVjh3UOj1/+8pfmvPPOa3J/IBAwXq/XPPjgg8Ft1dXVJjEx0fzhD3+IxIjWGDVqlLn22mtDto0bN87k5eUZY1jrcPjP2GnOmn744YdGktmyZUvwmNdee804HA7z+eeft/nMvIzVSocPH1Zpaalyc3OD25xOp3Jzc1VUVBTFyezi8/kkSampqZKk0tJS1dXVhax7VlaWevbsybq3wLRp0zRq1KiQ9ZRY53B5+eWXNWTIEF155ZVKS0vTwIED9fTTTwf379q1SxUVFSHr7Ha7lZ2dzTofp2HDhqmwsFA7duyQJL3//vvavHmzLrvsMkmsdVtozpoWFRUpJSVFQ4YMCR6Tm5srp9Op4uLiNp+RDwJtpS+//FINDQ3yeDwh2z0ejz7++OMoTWWXQCCg/Px8DR8+XGeddZYkqaKiQgkJCUpJSQk51uPxqKKiIgpTtl/Lly/Xe++9py1bthyxj3UOj08//VRPPvmkZs6cqV/96lfasmWLbrzxRiUkJGjSpEnBtTzaf0dY5+Mza9Ys+f1+ZWVlKS4uTg0NDZo3b57y8vIkibVuA81Z04qKCqWlpYXsj4+PV2pqakTWndhBzJs2bZq2b9+uzZs3R3sU65SXl+umm27SunXr1LFjx2iPY61AIKAhQ4bo3nvvlSQNHDhQ27dv18KFCzVp0qQoT2eX559/XkuXLtWyZct05plnatu2bcrPz1d6ejprfQLjZaxWOumkkxQXF3fEu1MqKyvl9XqjNJU9pk+frjVr1mjDhg3q0aNHcLvX69Xhw4dVXV0dcjzrfnxKS0tVVVWlQYMGKT4+XvHx8dq0aZPmz5+v+Ph4eTwe1jkMunfvrjPOOCNk2+mnn649e/ZIUnAt+e9I6912222aNWuWJkyYoH79+umnP/2pbr75ZhUUFEhirdtCc9bU6/WqqqoqZH99fb32798fkXUndlopISFBgwcPVmFhYXBbIBBQYWGhcnJyojhZ+2aM0fTp07Vq1SqtX79emZmZIfsHDx6sDh06hKx7WVmZ9uzZw7ofh4svvlgffPCBtm3bFnwMGTJEeXl5wX+zzq03fPjwI26dsGPHDvXq1UuSlJmZKa/XG7LOfr9fxcXFrPNx+vrrr+V0hv5pi4uLUyAQkMRat4XmrGlOTo6qq6tVWloaPGb9+vUKBALKzs5u+yHb/BLoE8Dy5ctNYmKiWbJkifnwww/N1KlTTUpKiqmoqIj2aO3W9ddfb9xut9m4caPZt29f8PH1118Hj/nFL35hevbsadavX2/effddk5OTY3JycqI4tR3+/d1YxrDO4VBSUmLi4+PNvHnzzM6dO83SpUtNp06dzHPPPRc85r777jMpKSnmpZdeMn/961/N5ZdfztuhW2DSpEnmlFNOCb71fOXKleakk04yt99+e/AY1vr4HThwwGzdutVs3brVSDIPP/yw2bp1q9m9e7cxpnlreumll5qBAwea4uJis3nzZtO3b1/eet7ePPbYY6Znz54mISHBDB061LzzzjvRHqldk3TUx+LFi4PHfPPNN+aGG24wXbt2NZ06dTJXXHGF2bdvX/SGtsR/xg7rHB6vvPKKOeuss0xiYqLJysoyixYtCtkfCATMnDlzjMfjMYmJiebiiy82ZWVlUZq2/fL7/eamm24yPXv2NB07djSnnnqqueOOO0xtbW3wGNb6+G3YsOGo/02eNGmSMaZ5a/rPf/7TTJw40XTp0sW4XC5zzTXXmAMHDkRkfocx/3ZbSQAAAMtwzQ4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOgHbBGKOpU6cqNTVVDodDKSkpys/PP67ncDgcWr16dZvMByB2xUd7AABojtdff11LlizRxo0bdeqpp8rpdCopKSmsP2Pjxo268MIL9dVXXyklJSWszw0geogdAO3C3//+d3Xv3l3Dhg2L9igA2hlexgIQ8yZPnqwZM2Zoz549cjgc6t27ty644IKQl7H27dunUaNGKSkpSZmZmVq2bJl69+6tRx55JOS5vvzyS11xxRXq1KmT+vbtq5dfflmS9I9//EMXXnihJKlr165yOByaPHlyhH5DAG2J2AEQ8x599FHdfffd6tGjh/bt26ctW7YccczPfvYz7d27Vxs3btSLL76oRYsWqaqq6ojj7rrrLl111VX661//qpEjRyovL0/79+9XRkaGXnzxRUlSWVmZ9u3bp0cffbTNfzcAbY/YARDz3G63kpOTFRcXJ6/Xq5NPPjlk/8cff6w33nhDTz/9tLKzszVo0CD99re/1TfffHPEc02ePFkTJ05Unz59dO+996qmpkYlJSWKi4tTamqqJCktLU1er1dutzsivx+AtkXsAGj3ysrKFB8fr0GDBgW39enTR127dj3i2P79+wf/3blzZ7lcrqOeAQJgD2IHwAmlQ4cOIV87HA4FAoEoTQMgEogdAO3e97//fdXX12vr1q3BbZ988om++uqr43qehIQESVJDQ0NY5wMQXcQOgHYvKytLubm5mjp1qkpKSrR161ZNnTpVSUlJcjgczX6eXr16yeFwaM2aNfriiy9UU1PThlMDiBRiB4AVnn32WXk8Hp1//vm64oorNGXKFCUnJ6tjx47Nfo5TTjlFd911l2bNmiWPx6Pp06e34cQAIsVhjDHRHgIAwu2zzz5TRkaG3njjDV188cXRHgdAFBE7AKywfv161dTUqF+/ftq3b59uv/12ff7559qxY8cRFyUDOLHwcREArFBXV6df/epX+vTTT5WcnKxhw4Zp6dKlhA4AzuwAAAC7cYEyAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGr/D5PJ4+n7iAa3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df.iloc[:,1],y=df.iloc[:,2],c=['green','green','green','red','red','red', 'black'])\n",
    "plt.xlabel('fight')\n",
    "plt.ylabel('kiss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'kiss')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7u3bt0lVXXSVJeumll9S7d2/95S9/0dKlS7V48eKmnA8AAOCCnFfsnDp1SgkJCZKkN954QzfffLMkKSsrS4cOHWq66QAAAC7QecXOZZddpvnz5+utt97SunXrdP3110uSDh48qI4dOzbpgAAAABfivGLnscce07PPPqsf/vCHGjNmjPr27StJeuWVV8JvbwEAAEQDlzHGnM8D6+vrFQwG1aFDh/C2jz76SG3btlVaWlqTDdgSgsGgPB6PAoGA3G630+MAAIBGaOzr93md2fniiy9UV1cXDp39+/friSeeUGVlZasLHQAAYLfzip1bbrlFS5YskSTV1tYqOztbv/vd7zRixAjNmzevSQcEAAC4EOcVO9u2bdMPfvADSdLLL78sr9er/fv3a8mSJZo7d26TDggAAHAhzit2Pv/8cyUnJ0uS/ud//kcjR45UTEyMrr76au3fv79JBwQAALgQ5xU73bt31+rVq1VVVaXXX39d1113nSSppqaGC3wBAEBUOa/YmTFjhu69915169ZN2dnZysnJkXTmLE+/fv2adEAAAIALcd4fPff7/Tp06JD69u2rmJgzzVReXi63262srKwmHbK58dFzAABan8a+fsed7w/w+Xzy+XwR2/hCQQAAEG0aHTsjR47U4sWL5Xa7deutt8rlcjV47MqVK5tkOAAAgAvV6NjxeDzhwElJSWnwuG+KIAAAgJbW6NhZtGhR+J+vu+46jRkz5qzH3XfffRc+FQAAQBM5r09j3XnnnXrttde+tr2oqEgvvPDCBQ8FAADQVM4rdpYuXaoxY8Zo8+bN4W133323li9frg0bNjTZcAAAABfqvGJn+PDheuaZZ3TzzTeroqJCd911l1auXKmNGze2uo+dAwAAu533R89/+tOfqra2VoMHD9bFF1+sTZs2qXv37k05GwAAwAVrdOwUFRWddfvFF1+s/v3765lnnglvmzNnzoVPBgAA0AQaHTvbt28/6/bu3bsrGAyG9/PRcwAAEE0aHTtceAwAAFqj87pAGQAAoLUgdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgtVYVO7NmzZLL5VJhYWF424kTJ1RQUKCOHTuqffv2GjVqlKqrq50bEgAARJVWEztbtmzRs88+q8svvzxi+6RJk/Tqq69qxYoV2rRpkw4ePKiRI0c6NCUAAIg2rSJ2jh07pry8PC1cuFAdOnQIbw8EAnruuec0Z84c/fM//7MGDBigRYsW6S9/+YvefvttBycGAADRolXETkFBgYYPH67c3NyI7RUVFTp16lTE9qysLHXp0kWlpaUNPl9dXZ2CwWDEDQAA2CnO6QG+zfLly7Vt2zZt2bLla/v8fr/i4+OVkpISsd3r9crv9zf4nMXFxXrooYeaelQAABCFovrMTlVVle655x4tXbpUiYmJTfa806ZNUyAQCN+qqqqa7LkBAEB0ierYqaioUE1Njfr376+4uDjFxcVp06ZNmjt3ruLi4uT1enXy5EnV1tZGPK66ulo+n6/B501ISJDb7Y64AQAAO0X121hDhw7Vu+++G7Ft/PjxysrK0v3336+MjAy1adNGJSUlGjVqlCSpsrJSBw4cUE5OjhMjAwCAKBPVsZOcnKzevXtHbGvXrp06duwY3n7HHXeoqKhIqampcrvduvvuu5WTk6Orr77aiZEBAECUierYaYx///d/V0xMjEaNGqW6ujoNGzZMzzzzjNNjAQCAKOEyxhinh3BaMBiUx+NRIBDg+h0AAFqJxr5+R/UFygAAABeK2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYLapjp7i4WFdeeaWSk5OVlpamESNGqLKyMuKYEydOqKCgQB07dlT79u01atQoVVdXOzQxAACINlEdO5s2bVJBQYHefvttrVu3TqdOndJ1112n48ePh4+ZNGmSXn31Va1YsUKbNm3SwYMHNXLkSAenBgAA0cRljDFOD9FYn376qdLS0rRp0yZdc801CgQCuvjii7Vs2TL95Cc/kSR98MEH6tmzp0pLS3X11Vc36nmDwaA8Ho8CgYDcbndz/goAAKCJNPb1O6rP7HxVIBCQJKWmpkqSKioqdOrUKeXm5oaPycrKUpcuXVRaWtrg89TV1SkYDEbcAACAnVpN7IRCIRUWFmrw4MHq3bu3JMnv9ys+Pl4pKSkRx3q9Xvn9/gafq7i4WB6PJ3zLyMhoztEBAICDWk3sFBQUaNeuXVq+fPkFP9e0adMUCATCt6qqqiaYEAAARKM4pwdojIkTJ2rNmjV688031blz5/B2n8+nkydPqra2NuLsTnV1tXw+X4PPl5CQoISEhOYcGQAARImoPrNjjNHEiRO1atUqrV+/XpmZmRH7BwwYoDZt2qikpCS8rbKyUgcOHFBOTk5LjwsAAKJQVJ/ZKSgo0LJly/THP/5RycnJ4etwPB6PkpKS5PF4dMcdd6ioqEipqalyu926++67lZOT0+hPYgEAALtF9UfPXS7XWbcvWrRI48aNk3TmSwUnT56sF198UXV1dRo2bJieeeaZb3wb66v46DkAAK1PY1+/ozp2WgqxAwBA62Pl9+wAAACcK2IHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAADQ5IwxmrV5ljyzPEp8JFHjV4/XidMnHJklzpGfCgAArLbs3WWaVjItfH/JO0vUPqG9nrrhqRafhTM7AACgyb3+t9cV64oN3w+ZkP7f7v/nyCzEDgAAaHIdEjvI5XKF78coRqlJqY7MQuwAAIAmV5RTJE+CR7GuWMXFxCkmJkazcmc5MgvX7AAAgCbXNaWr3rnzHS3ZuUQnTp/QiKwRusJ3hSOzuIwxxpGfHEWCwaA8Ho8CgYDcbrfT4wAAgEZo7Os3b2MBAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADgvBhjVB+qd3qMb0XsAACAc2KM0fT109X20bZKeCRBeSvz9MWpL5weq0HEDgAAOCfPbX9Oj7z1iE6cPqF6U6/lu5br/jfud3qsBhE7AADgnLzx4RuKcf09IUImpLV71zo40TcjdgAAwDm5qO1FEbET44pRWrs0Byf6ZsQOAAA4J/cNuk8dEjsoxhWjWFes2sS00azcWU6P1aA4pwcAAACtS9eUrnrnznf04rsvqq6+TiOyRijroiynx2oQsQMAAM6Zr71Pk3ImOT1Go/A2FgAAsBqxAwAArEbsAAAAqxE7AADgGx3+/LDyVubp+7//vn687Mfae2Sv0yOdEy5QBgAADQqZkG544QZt929XvanX3478TUOeH6IPJn6glMQUp8drFM7sAACABu09sldbD21VvTnzBz/rTb2qj1dr00ebHJ6s8YgdAADQoPjY+HPaHo2IHQAA0KCunq665fu3yPV//4l1xap3Wm9dm3mt06M1GtfsAACABrlcLr1020t6/M+Pa7t/uzJTMvXADx5QYlyi06M1mjVndp5++ml169ZNiYmJys7OVnl5udMjAQBghfjYeD14zYN6+faX9fh1j6tDUgenRzonVsTOf//3f6uoqEi/+c1vtG3bNvXt21fDhg1TTU2N06MBAACHWRE7c+bM0YQJEzR+/Hj16tVL8+fPV9u2bfX88887PRoAAHBYq4+dkydPqqKiQrm5ueFtMTExys3NVWlp6VkfU1dXp2AwGHEDAAB2avWxc/jwYdXX18vr9UZs93q98vv9Z31McXGxPB5P+JaRkdESowIAAAe0+tg5H9OmTVMgEAjfqqqqnB4JAAA0k1b/0fOLLrpIsbGxqq6ujtheXV0tn8931sckJCQoISGhJcYDAAAOa/VnduLj4zVgwACVlJSEt4VCIZWUlCgnJ8fByQAAQDRo9Wd2JKmoqEhjx47VwIEDddVVV+mJJ57Q8ePHNX78eKdHAwAADrMidv7lX/5Fn376qWbMmCG/368rrrhCa9eu/dpFywAA4LvHZYwxTg/htGAwKI/Ho0AgILfb7fQ4AACgERr7+t3qr9kBAAD4JsQOAACwmhXX7FyoL9/J45uUAQBoPb583f62K3KIHUlHjx6VJL5JGQCAVujo0aPyeDwN7ucCZZ35Xp6DBw8qOTlZLpfrvJ8nGAwqIyNDVVVVXOjcjFjnlsE6twzWuWWwzi2nJdfaGKOjR48qPT1dMTENX5nDmR2d+cOhnTt3brLnc7vd/J+pBbDOLYN1bhmsc8tgnVtOS631N53R+RIXKAMAAKsROwAAwGrEThNKSEjQb37zG/7IaDNjnVsG69wyWOeWwTq3nGhcay5QBgAAVuPMDgAAsBqxAwAArEbsAAAAqxE7AADAasROE3n66afVrVs3JSYmKjs7W+Xl5U6P1KoVFxfryiuvVHJystLS0jRixAhVVlZGHHPixAkVFBSoY8eOat++vUaNGqXq6mqHJrbDrFmz5HK5VFhYGN7GOjeNTz75RD/72c/UsWNHJSUlqU+fPtq6dWt4vzFGM2bMUKdOnZSUlKTc3Fzt2bPHwYlbp/r6ek2fPl2ZmZlKSkrS9773Pf32t7+N+NtJrPW5e/PNN3XTTTcpPT1dLpdLq1evjtjfmDU9cuSI8vLy5Ha7lZKSojvuuEPHjh1rmV/A4IItX77cxMfHm+eff9789a9/NRMmTDApKSmmurra6dFarWHDhplFixaZXbt2mR07dpgbb7zRdOnSxRw7dix8zK9+9SuTkZFhSkpKzNatW83VV19tBg0a5ODUrVt5ebnp1q2bufzyy80999wT3s46X7gjR46Yrl27mnHjxpmysjLz4Ycfmtdff93s3bs3fMysWbOMx+Mxq1evNjt37jQ333yzyczMNF988YWDk7c+M2fONB07djRr1qwx+/btMytWrDDt27c3Tz75ZPgY1vrc/elPfzIPPvigWblypZFkVq1aFbG/MWt6/fXXm759+5q3337bvPXWW6Z79+5mzJgxLTI/sdMErrrqKlNQUBC+X19fb9LT001xcbGDU9mlpqbGSDKbNm0yxhhTW1tr2rRpY1asWBE+5v333zeSTGlpqVNjtlpHjx41PXr0MOvWrTP/9E//FI4d1rlp3H///WbIkCEN7g+FQsbn85nHH388vK22ttYkJCSYF198sSVGtMbw4cPNL3/5y4htI0eONHl5ecYY1ropfDV2GrOm7733npFktmzZEj7mtddeMy6Xy3zyySfNPjNvY12gkydPqqKiQrm5ueFtMTExys3NVWlpqYOT2SUQCEiSUlNTJUkVFRU6depUxLpnZWWpS5curPt5KCgo0PDhwyPWU2Kdm8orr7yigQMH6rbbblNaWpr69eunhQsXhvfv27dPfr8/Yp09Ho+ys7NZ53M0aNAglZSUaPfu3ZKknTt3avPmzbrhhhsksdbNoTFrWlpaqpSUFA0cODB8TG5urmJiYlRWVtbsM/KHQC/Q4cOHVV9fL6/XG7Hd6/Xqgw8+cGgqu4RCIRUWFmrw4MHq3bu3JMnv9ys+Pl4pKSkRx3q9Xvn9fgembL2WL1+ubdu2acuWLV/bxzo3jQ8//FDz5s1TUVGRHnjgAW3ZskW//vWvFR8fr7Fjx4bX8mz/HmGdz83UqVMVDAaVlZWl2NhY1dfXa+bMmcrLy5Mk1roZNGZN/X6/0tLSIvbHxcUpNTW1Rdad2EHUKygo0K5du7R582anR7FOVVWV7rnnHq1bt06JiYlOj2OtUCikgQMH6tFHH5Uk9evXT7t27dL8+fM1duxYh6ezy0svvaSlS5dq2bJluuyyy7Rjxw4VFhYqPT2dtf4O422sC3TRRRcpNjb2a59Oqa6uls/nc2gqe0ycOFFr1qzRhg0b1Llz5/B2n8+nkydPqra2NuJ41v3cVFRUqKamRv3791dcXJzi4uK0adMmzZ07V3FxcfJ6vaxzE+jUqZN69eoVsa1nz546cOCAJIXXkn+PXLj77rtPU6dO1ejRo9WnTx/9/Oc/16RJk1RcXCyJtW4OjVlTn8+nmpqaiP2nT5/WkSNHWmTdiZ0LFB8frwEDBqikpCS8LRQKqaSkRDk5OQ5O1roZYzRx4kStWrVK69evV2ZmZsT+AQMGqE2bNhHrXllZqQMHDrDu52Do0KF69913tWPHjvBt4MCBysvLC/8z63zhBg8e/LWvTti9e7e6du0qScrMzJTP54tY52AwqLKyMtb5HH3++eeKiYl8aYuNjVUoFJLEWjeHxqxpTk6OamtrVVFRET5m/fr1CoVCys7Obv4hm/0S6O+A5cuXm4SEBLN48WLz3nvvmfz8fJOSkmL8fr/To7Vad955p/F4PGbjxo3m0KFD4dvnn38ePuZXv/qV6dKli1m/fr3ZunWrycnJMTk5OQ5ObYd//DSWMaxzUygvLzdxcXFm5syZZs+ePWbp0qWmbdu25oUXXggfM2vWLJOSkmL++Mc/mnfeecfccsstfBz6PIwdO9Zccskl4Y+er1y50lx00UVmypQp4WNY63N39OhRs337drN9+3YjycyZM8ds377d7N+/3xjTuDW9/vrrTb9+/UxZWZnZvHmz6dGjBx89b22eeuop06VLFxMfH2+uuuoq8/bbbzs9Uqsm6ay3RYsWhY/54osvzF133WU6dOhg2rZta2699VZz6NAh54a2xFdjh3VuGq+++qrp3bu3SUhIMFlZWWbBggUR+0OhkJk+fbrxer0mISHBDB061FRWVjo0besVDAbNPffcY7p06WISExPNpZdeah588EFTV1cXPoa1PncbNmw467+Tx44da4xp3Jr+7//+rxkzZoxp3769cbvdZvz48ebo0aMtMr/LmH/4WkkAAADLcM0OAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDoBWwRij/Px8paamyuVyKSUlRYWFhef0HC6XS6tXr26W+QBErzinBwCAxli7dq0WL16sjRs36tJLL1VMTIySkpKa9Gds3LhR1157rT777DOlpKQ06XMDcA6xA6BV+Nvf/qZOnTpp0KBBTo8CoJXhbSwAUW/cuHG6++67deDAAblcLnXr1k0//OEPI97GOnTokIYPH66kpCRlZmZq2bJl6tatm5544omI5zp8+LBuvfVWtW3bVj169NArr7wiSfroo4907bXXSpI6dOggl8ulcePGtdBvCKA5ETsAot6TTz6phx9+WJ07d9ahQ4e0ZcuWrx3zi1/8QgcPHtTGjRv1hz/8QQsWLFBNTc3XjnvooYd0++2365133tGNN96ovLw8HTlyRBkZGfrDH/4gSaqsrNShQ4f05JNPNvvvBqD5ETsAop7H41FycrJiY2Pl8/l08cUXR+z/4IMP9MYbb2jhwoXKzs5W//799R//8R/64osvvvZc48aN05gxY9S9e3c9+uijOnbsmMrLyxUbG6vU1FRJUlpamnw+nzweT4v8fgCaF7EDoNWrrKxUXFyc+vfvH97WvXt3dejQ4WvHXn755eF/bteundxu91nPAAGwB7ED4DulTZs2EfddLpdCoZBD0wBoCcQOgFbv+9//vk6fPq3t27eHt+3du1efffbZOT1PfHy8JKm+vr5J5wPgLGIHQKuXlZWl3Nxc5efnq7y8XNu3b1d+fr6SkpLkcrka/Txdu3aVy+XSmjVr9Omnn+rYsWPNODWAlkLsALDCkiVL5PV6dc011+jWW2/VhAkTlJycrMTExEY/xyWXXKKHHnpIU6dOldfr1cSJE5txYgAtxWWMMU4PAQBN7eOPP1ZGRobeeOMNDR061OlxADiI2AFghfXr1+vYsWPq06ePDh06pClTpuiTTz7R7t27v3ZRMoDvFv5cBAArnDp1Sg888IA+/PBDJScna9CgQVq6dCmhA4AzOwAAwG5coAwAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACw2v8HA5fVEW+tq4sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.iloc[:,1], df.iloc[:,2], marker='.', c=['red','red','red','green','green','green','black'])\n",
    "plt.xlabel('fight')\n",
    "plt.ylabel('kiss')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.9 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "774a858b74b9162cfeaabab2cd52460f9ad2aea53b492b6366c174bcba156af9"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
